{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"train-colab-stable-bccd.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"0bVtSWKRUp7q","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1596072347577,"user_tz":-480,"elapsed":884,"user":{"displayName":"李智揚","photoUrl":"","userId":"08321859986425279811"}},"outputId":"cced3921-e923-4dbb-b3c7-1b11772d75f5"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MRDGiFNUyeZ5","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1596072348809,"user_tz":-480,"elapsed":734,"user":{"displayName":"李智揚","photoUrl":"","userId":"08321859986425279811"}}},"source":["import sys\n","sys.path.append('/content/drive/My Drive/yolo-v4-tf.keras')"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"e3tVRS6-ybFh","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1596072353411,"user_tz":-480,"elapsed":4038,"user":{"displayName":"李智揚","photoUrl":"","userId":"08321859986425279811"}},"outputId":"3d3c97a5-2100-47ec-9a0e-ba2a3e24f8da"},"source":["import cv2\n","import numpy as np\n","from utils import DataGenerator, preprocess_true_boxes\n","import matplotlib.pyplot as plt\n","import tensorflow.keras.backend as K\n","import tensorflow as tf\n","import math\n","from models import Yolov4, yolov4_head, get_boxes, nms\n","from config import yolo_config\n","from loss import *\n","print(tf.__version__)"],"execution_count":3,"outputs":[{"output_type":"stream","text":["2.2.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SSIg2-CJy5NY","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":92},"executionInfo":{"status":"ok","timestamp":1596072374516,"user_tz":-480,"elapsed":14696,"user":{"displayName":"李智揚","photoUrl":"","userId":"08321859986425279811"}},"outputId":"deef0fa8-a2d1-4215-c91d-5d2555c27745"},"source":["with open('/content/drive/My Drive/yolo-v4-tf.keras/dataset/train_txt/anno2.txt') as f:\n","# with open('/content/drive/My Drive/yolo-v4-tf.keras/dataset/train_txt/anno.txt') as f:\n","    lines = f.readlines()\n","lines = lines[:]\n","# lines = lines * 8\n","print(lines)\n","# lines = lines * 32\n","\n","NUM_CLASS = 3\n","FOLDER_PATH = '/content/drive/My Drive/yolo-v4-tf.keras'\n","BS = 8\n","anchors = np.array([12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401]).reshape((-1, 2))\n","\n","data_gen = DataGenerator(lines[:], BS, (416, 416), num_classes=NUM_CLASS, folder_path=FOLDER_PATH, anchors=anchors)\n","model = Yolov4(\n","                weight_path=None,\n","                class_name_path='/content/drive/My Drive/yolo-v4-tf.keras/bccd_classes.txt'\n","            #    class_name_path='/content/drive/My Drive/yolo-v4-tf.keras/coco_classes.txt',\n","#               weight_path='yolov4.weights',\n","#                img_size=(416, 416, 3),\n","              )\n","model.build_model(load_pretrained=False)\n","\n","print('num class : ', model.num_classes)\n","\n","# model2 = tf.keras.models.load_model('/content/drive/My Drive/bccd.h5', compile=False)\n","# model.yolo_model = \n","# yolov4_output = yolov4_head(self.yolo_model.output, self.num_classes, self.anchors, self.xyscale)\n","# self.inference_model = models.Model(self.yolo_model.input,\n","#                                     nms(yolov4_output, self.img_size, self.num_classes))  # [boxes, scores, classes, valid_detections]"],"execution_count":4,"outputs":[{"output_type":"stream","text":["['dataset/train_img/BloodImage_00000.jpg 260,177,491,376,0 78,336,184,435,2 63,237,169,336,2 214,362,320,461,2 414,352,506,445,2 555,356,640,455,2 469,412,567,480,2 1,333,87,437,2 4,406,95,480,2 155,74,247,174,2 11,84,104,162,2 534,39,639,139,2 547,195,640,295,2 388,11,481,111,2 171,175,264,275,2 260,1,374,83,2 229,91,343,174,2 69,144,184,235,2 482,131,594,230,2 368,89,464,176,2\\n', 'dataset/train_img/BloodImage_00001.jpg 68,315,286,480,0 346,361,446,454,2 53,179,146,299,2 449,400,536,480,2 461,132,548,212,2 454,295,541,375,2 417,283,508,383,2 278,342,369,451,2 545,62,636,159,2 485,91,576,188,2 376,171,438,253,2 329,177,395,271,2 291,59,407,168,2 299,1,404,68,2 346,26,449,138,2 134,1,241,95,2 1,38,98,164,2 165,160,257,264,2 464,209,566,319,2\\n', 'dataset/train_img/BloodImage_00002.jpg 385,98,523,198,2 384,164,499,260,2 101,120,224,222,2 130,344,234,443,2 161,381,254,480,2 14,228,123,344,2 306,293,415,409,2 531,103,632,221,2 492,233,593,347,2 364,261,465,375,2 264,60,365,174,2 249,174,369,296,2 283,1,567,106,0 109,1,202,98,2 135,66,228,164,2 480,374,590,480,2\\n', 'dataset/train_img/BloodImage_00003.jpg 127,40,344,226,0 317,93,424,195,2 379,146,471,234,2 504,210,614,302,2 417,248,553,369,2 514,13,615,136,2 417,1,520,88,2 1,126,79,240,2 47,202,158,317,2 314,284,424,377,2 365,350,484,451,2 1,347,86,434,2 49,396,183,480,2 514,385,595,477,2 56,66,151,211,2 213,253,333,368,2 335,268,370,299,1\\n', 'dataset/train_img/BloodImage_00004.jpg 109,134,324,321,0 432,242,528,325,2 510,112,606,195,2 482,361,594,475,2 75,281,166,375,2 38,1,147,62,2 288,33,390,131,2 411,66,527,185,2 1,82,102,198,2 1,283,81,372,2 1,315,78,397,2 391,373,469,455,2 127,47,163,81,1\\n', 'dataset/train_img/BloodImage_00005.jpg 273,115,386,222,2 56,103,169,210,2 414,42,518,170,2 536,74,640,202,2 474,193,578,321,2 466,337,570,465,2 420,352,524,480,2 215,174,314,281,2 380,181,479,288,2 1,64,99,171,2 324,1,444,87,2 162,1,290,70,2 173,165,260,270,2 139,191,226,296,2 103,211,190,316,2 29,231,116,336,2 170,65,207,100,1 6,401,43,436,1 124,305,161,340,1 230,288,441,480,0 151,298,236,423,2 63,19,174,97,2\\n', 'dataset/train_img/BloodImage_00006.jpg 117,190,290,360,0 89,184,131,221,1 65,203,107,240,1 333,1,372,30,1 79,357,202,459,2 495,1,620,114,2 353,327,476,463,2 449,319,565,393,2 291,104,403,194,2 211,76,323,166,2 207,350,319,440,2 253,298,371,403,2 546,368,639,480,2 482,223,617,327,2 503,160,619,246,2 373,142,489,235,2 108,121,235,216,2 16,59,143,154,2\\n', 'dataset/train_img/BloodImage_00007.jpg 193,92,387,285,0 17,298,134,402,2 64,372,175,479,2 119,330,230,437,2 169,265,259,374,2 191,291,281,400,2 213,305,303,414,2 247,315,337,424,2 495,300,601,402,2 534,371,640,473,2 510,7,616,109,2 515,44,639,152,2 18,124,142,232,2 70,63,194,171,2 19,29,143,137,2 270,1,392,71,2 254,427,340,480,2 298,434,381,480,2\\n', 'dataset/train_img/BloodImage_00008.jpg 42,183,277,381,0 398,222,519,330,2 505,103,626,211,2 366,315,472,413,2 456,312,562,410,2 524,250,630,348,2 534,335,640,433,2 353,43,459,141,2 316,43,422,141,2 265,25,371,123,2 221,18,327,116,2 109,1,221,85,2 1,166,96,299,2 263,402,365,480,2 178,402,280,480,2 1,1,102,77,2 196,135,313,215,2 447,392,607,480,2 148,62,234,172,2 438,1,564,85,2\\n', 'dataset/train_img/BloodImage_00009.jpg 23,137,255,423,0 441,324,575,423,2 372,192,468,279,2 305,213,401,300,2 226,288,324,439,2 313,318,408,414,2 282,384,377,480,2 446,97,567,206,2 440,1,561,96,2 214,107,331,198,2 298,85,435,211,2 7,76,133,197,2 1,1,115,94,2 286,1,394,65,2 154,13,289,127,2 452,415,493,449,1 479,155,575,242,2 135,393,231,480,2\\n', 'dataset/train_img/BloodImage_00010.jpg 23,229,204,421,0 239,253,510,470,0 167,212,269,331,2 69,138,170,243,2 43,84,126,171,2 275,166,358,253,2 311,179,394,266,2 267,69,350,156,2 307,75,390,162,2 339,80,422,167,2 456,1,550,68,2 314,1,429,69,2 200,1,310,43,2 16,1,100,79,2 406,80,538,185,2 560,236,640,345,2 571,174,640,260,2 566,76,640,160,2\\n', 'dataset/train_img/BloodImage_00011.jpg 109,119,304,332,0 364,201,453,279,2 416,167,505,245,2 459,111,548,189,2 462,142,551,240,2 505,163,576,240,2 476,235,583,332,2 14,296,117,409,2 220,8,313,112,2 342,48,435,152,2 317,74,434,180,2 258,79,352,171,2 404,291,498,394,2 199,337,293,440,2 85,372,211,477,2 104,1,212,117,2 85,153,122,196,1 262,419,354,480,2 1,77,94,180,2\\n', 'dataset/train_img/BloodImage_00012.jpg 2,187,228,359,0 349,146,475,240,2 377,210,483,314,2 401,299,507,403,2 296,281,386,387,2 251,311,344,439,2 168,304,264,402,2 14,335,125,434,2 126,393,251,479,2 329,376,412,477,2 519,356,622,455,2 476,257,579,356,2 489,193,592,292,2 564,232,640,328,2 417,403,463,440,1 351,44,397,81,1 220,20,303,107,2 495,82,605,204,2 299,431,381,480,2\\n', 'dataset/train_img/BloodImage_00013.jpg 31,208,238,406,0 255,283,371,392,2 405,288,517,420,2 369,77,486,223,2 90,98,200,189,2 203,365,313,456,2 318,396,427,480,2 574,160,639,281,2 466,57,564,174,2 547,48,638,144,2 500,214,599,325,2 184,12,299,118,2 105,34,220,140,2 455,404,555,480,2 324,402,424,478,2\\n', 'dataset/train_img/BloodImage_00014.jpg 236,211,379,353,0 158,140,258,234,2 78,334,197,435,2 1,137,72,242,2 33,89,152,189,2 53,15,172,115,2 336,1,406,93,2 297,78,422,173,2 224,76,352,191,2 215,380,334,480,2 356,303,447,393,2 393,215,484,305,2 448,406,545,480,2 431,1,554,102,2 362,389,405,428,1 233,340,276,379,1 542,289,630,399,2 579,131,640,236,2 525,70,618,176,2\\n', 'dataset/train_img/BloodImage_00015.jpg 145,4,361,240,0 353,135,428,232,2 354,12,448,128,2 440,78,540,160,2 537,47,638,144,2 435,1,538,75,2 411,359,522,459,2 266,409,374,480,2 1,396,134,480,2 1,272,102,379,2 62,62,165,169,2 248,201,351,308,2 91,300,204,391,2 564,140,640,240,2 291,373,337,410,1 373,202,484,302,2\\n', 'dataset/train_img/BloodImage_00016.jpg 151,118,329,279,0 121,323,242,440,2 324,115,418,236,2 450,329,542,442,2 427,364,519,477,2 230,333,346,449,2 448,91,549,168,2 570,290,640,403,2 523,69,637,174,2 334,219,451,337,2 449,227,566,345,2 66,155,160,276,2 97,327,129,365,1\\n', 'dataset/train_img/BloodImage_00017.jpg 128,215,336,391,0 406,176,514,272,2 400,73,510,181,2 215,87,299,187,2 435,302,547,400,2 424,354,536,452,2 131,382,235,470,2 481,164,585,252,2 5,335,102,434,2 1,101,53,205,2 221,390,340,464,2 156,1,260,84,2 279,37,381,145,2 528,1,621,87,2 488,11,581,98,2 4,245,106,353,2 322,335,424,443,2 382,47,419,79,1\\n', 'dataset/train_img/BloodImage_00018.jpg 302,1,481,166,0 167,100,307,197,2 210,1,319,64,2 561,74,639,183,2 130,26,230,125,2 74,189,164,277,2 304,162,394,250,2 363,163,453,251,2 410,152,500,240,2 430,131,520,219,2 483,109,573,197,2 541,276,631,364,2 572,181,640,287,2 286,424,375,480,2 308,240,421,325,2 189,345,278,423,2 1,213,89,340,2 67,324,182,447,2 17,45,131,168,2 21,1,114,86,2 243,189,333,277,2 241,259,331,347,2\\n', 'dataset/train_img/BloodImage_00019.jpg 107,97,295,267,0 41,158,126,280,2 410,315,513,430,2 524,332,627,447,2 461,348,564,463,2 227,398,343,480,2 133,398,249,480,2 125,366,241,448,2 373,150,476,243,2 492,196,595,289,2 297,201,428,315,2 532,48,615,142,2 1,122,56,255,2 88,1,237,93,2 231,251,317,336,2 164,284,270,376,2 320,133,423,226,2\\n', 'dataset/train_img/BloodImage_00020.jpg 419,189,478,236,1 352,364,475,447,0 226,99,333,206,2 404,25,511,132,2 511,38,618,145,2 457,206,564,313,2 478,370,585,477,2 287,238,394,345,2 372,343,406,374,1 40,277,147,384,2 1,277,79,379,2 80,166,187,273,2 143,245,250,352,2 144,366,251,473,2 142,364,249,471,2 109,41,216,148,2 460,59,567,166,2 570,256,640,357,2 61,383,169,480,2 1,128,106,235,2 40,1,149,95,2 165,1,274,95,2 80,128,115,161,1\\n', 'dataset/train_img/BloodImage_00021.jpg 330,302,494,438,0 375,103,422,146,1 483,184,530,227,1 363,141,483,255,2 274,161,394,275,2 215,219,335,333,2 97,335,217,449,2 5,262,125,376,2 50,229,170,343,2 1,102,104,197,2 72,98,176,193,2 521,109,625,204,2 581,100,640,194,2 378,1,475,76,2 209,1,318,111,2 117,1,207,87,2 568,347,639,461,2 168,84,288,198,2 221,361,341,475,2\\n', 'dataset/train_img/BloodImage_00022.jpg 108,194,281,372,0 99,329,138,360,1 149,1,188,30,1 394,199,433,230,1 17,163,130,255,2 1,197,113,289,2 289,142,392,234,2 340,81,443,173,2 275,22,392,126,2 441,59,579,163,2 471,143,579,263,2 557,359,639,480,2 406,374,538,480,2 272,389,389,480,2 378,249,482,349,2 15,12,100,147,2\\n', 'dataset/train_img/BloodImage_00023.jpg 98,34,281,206,0 277,113,387,233,2 388,82,498,202,2 306,243,416,363,2 186,268,291,381,2 296,364,423,480,2 511,147,638,263,2 62,414,174,480,2 37,1,141,76,2 29,152,117,247,2 494,1,594,74,2 572,264,640,345,2 524,354,640,453,2 15,220,109,334,2 132,292,171,340,1 343,1,468,110,2 209,245,305,327,2 177,219,273,301,2\\n', 'dataset/train_img/BloodImage_00024.jpg 310,1,471,128,0 89,89,192,206,2 265,114,368,231,2 203,176,295,318,2 109,195,204,306,2 391,117,486,228,2 376,250,471,361,2 314,357,419,477,2 25,309,142,428,2 497,1,604,97,2 578,144,640,248,2 570,252,640,390,2 494,325,588,427,2 399,334,517,457,2 19,234,103,344,2 484,196,587,313,2\\n', 'dataset/train_img/BloodImage_00026.jpg 387,1,564,102,0 178,2,287,113,2 114,154,223,265,2 102,60,211,171,2 42,70,151,181,2 1,112,94,227,2 2,171,113,287,2 424,270,535,386,2 173,362,282,460,2 86,358,195,456,2 253,151,350,245,2 497,196,608,294,2 317,351,426,471,2 202,280,329,394,2 295,202,422,316,2 436,94,574,210,2 294,78,407,194,2 146,269,186,304,1 282,425,322,460,1\\n', 'dataset/train_img/BloodImage_00028.jpg 255,1,430,160,0 224,177,316,272,2 328,290,431,391,2 203,308,306,409,2 1,281,107,404,2 143,118,250,241,2 1,98,70,217,2 1,185,82,272,2 89,243,171,330,2 129,409,246,480,2 166,221,269,322,2 119,1,220,68,2 192,1,272,60,2 387,167,491,266,2 441,195,505,267,2 553,181,639,285,2 534,228,620,332,2 553,362,639,480,2 553,1,640,104,2 462,58,583,190,2 385,75,501,181,2 266,328,388,450,2\\n', 'dataset/train_img/BloodImage_00029.jpg 75,179,224,315,0 440,283,495,317,1 241,86,358,199,2 212,48,293,142,2 364,143,463,228,2 424,137,545,228,2 227,193,348,300,2 207,265,328,372,2 139,320,260,427,2 256,365,377,472,2 390,356,511,463,2 506,110,624,196,2 517,57,638,148,2 421,8,526,107,2 70,23,200,145,2 19,70,129,186,2 1,128,87,251,2 1,194,79,318,2 1,264,75,366,2 1,310,75,412,2 24,355,105,459,2 11,423,103,480,2 207,1,300,51,2 275,1,365,74,2\\n', 'dataset/train_img/BloodImage_00030.jpg 1,191,143,333,0 123,249,230,370,2 149,113,229,219,2 198,117,278,223,2 195,58,264,144,2 291,270,383,377,2 324,402,429,480,2 359,402,464,480,2 375,239,472,340,2 457,191,554,292,2 299,174,415,277,2 458,387,556,480,2 1,322,75,425,2 1,63,67,191,2 297,1,428,81,2 500,238,597,339,2 485,281,582,382,2 473,338,570,439,2 572,356,640,455,2 525,7,640,148,2 445,35,558,159,2 347,91,389,137,1 405,86,447,132,1 301,23,343,69,1 286,123,396,229,2\\n', 'dataset/train_img/BloodImage_00031.jpg 127,143,270,273,0 287,364,437,480,0 153,383,257,480,2 47,358,151,455,2 83,321,187,418,2 332,153,436,250,2 395,228,514,329,2 520,396,631,480,2 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298,20,392,122,2 11,52,128,159,2 191,1,292,78,2 414,1,515,78,2 128,67,410,296,0\\n', 'dataset/train_img/BloodImage_00393.jpg 398,290,519,416,2 141,240,275,366,2 1,189,120,278,2 1,373,86,480,2 125,15,247,124,2 105,122,227,231,2 276,394,388,479,2 539,12,624,95,2 354,20,485,134,2 256,1,369,104,2 321,271,431,378,2 210,92,438,297,0\\n', 'dataset/train_img/BloodImage_00395.jpg 25,90,127,209,2 6,1,108,98,2 109,1,232,77,2 274,6,389,118,2 364,173,479,289,2 86,266,183,368,2 1,314,96,424,2 177,331,273,441,2 283,306,380,406,2 392,296,504,397,2 501,370,613,471,2 388,62,486,163,2 233,412,324,479,2 90,412,202,478,2 114,66,351,294,0 472,201,540,268,1 470,43,516,84,1\\n', 'dataset/train_img/BloodImage_00396.jpg 474,110,596,231,2 429,259,537,382,2 121,141,214,245,2 295,25,388,129,2 471,1,572,102,2 1,285,101,387,2 163,236,264,338,2 101,313,214,411,2 213,321,326,419,2 543,286,640,386,2 398,59,495,159,2 106,387,198,461,2 2,20,129,120,2 213,98,283,231,2 347,357,449,460,2 16,174,118,277,2 539,402,640,480,2 437,426,492,479,1 302,400,337,423,1 235,138,477,355,0\\n', 'dataset/train_img/BloodImage_00397.jpg 68,154,165,249,2 1,145,66,260,2 207,160,334,270,2 435,347,540,437,2 535,356,639,464,2 446,221,550,329,2 509,94,640,207,2 456,3,560,111,2 354,144,458,252,2 169,317,277,455,2 64,302,166,406,2 74,200,176,304,2 141,18,217,109,2 316,47,409,141,2 356,1,465,56,2 35,65,128,159,2 66,371,181,463,2 247,352,462,480,0\\n', 'dataset/train_img/BloodImage_00398.jpg 1,260,89,378,2 72,105,182,229,2 340,215,429,318,2 206,247,320,330,2 287,311,409,412,2 167,348,289,449,2 351,6,447,103,2 404,86,532,171,2 450,374,562,480,2 435,259,562,362,2 23,377,142,480,2 160,10,256,107,2 509,147,605,244,2 474,1,571,77,2 187,44,394,265,0\\n', 'dataset/train_img/BloodImage_00400.jpg 462,368,597,462,2 376,104,510,208,2 219,224,347,315,2 124,286,222,390,2 69,393,174,479,2 219,325,327,425,2 293,291,416,409,2 362,203,470,303,2 35,1,131,115,2 461,195,561,296,2 374,1,482,100,2 1,110,76,242,2 117,18,355,235,0\\n', 'dataset/train_img/BloodImage_00402.jpg 11,43,111,146,2 1,166,107,293,2 93,327,189,425,2 108,176,224,286,2 221,41,337,151,2 348,377,440,480,2 171,377,271,480,2 329,147,447,250,2 459,29,538,123,2 508,148,614,251,2 546,41,638,133,2 337,250,454,368,2 262,135,362,232,2 296,243,341,291,1 454,268,640,457,0\\n', 'dataset/train_img/BloodImage_00403.jpg 454,197,581,300,2 303,284,374,389,2 339,159,466,262,2 337,157,464,260,2 323,120,407,201,2 234,84,361,187,2 211,1,317,108,2 1,14,90,122,2 1,138,63,239,2 210,336,316,444,2 9,371,112,469,2 11,272,114,370,2 78,368,183,449,2 490,10,590,116,2 556,126,640,197,2 70,165,287,368,0 369,260,431,325,1 564,1,615,44,1\\n', 'dataset/train_img/BloodImage_00404.jpg 398,196,529,282,2 416,53,537,168,2 543,62,640,177,2 279,43,383,147,2 188,355,292,447,2 284,348,360,434,2 339,306,440,407,2 397,344,522,433,2 434,296,559,385,2 1,364,93,468,2 70,71,190,157,2 185,1,306,70,2 574,179,640,288,2 115,134,363,369,0 24,291,71,327,1\\n', 'dataset/train_img/BloodImage_00405.jpg 53,306,162,412,2 1,203,68,322,2 162,114,271,220,2 110,82,221,197,2 151,1,245,89,2 243,1,337,89,2 509,206,613,303,2 479,105,583,202,2 376,142,480,239,2 238,235,360,338,2 176,212,291,315,2 158,320,256,449,2 307,336,378,455,2 316,1,451,153,2 338,248,562,450,0\\n', 'dataset/train_img/BloodImage_00407.jpg 30,168,140,264,2 12,35,109,133,2 25,352,137,452,2 109,318,221,418,2 158,240,273,361,2 248,346,345,441,2 350,292,453,382,2 329,58,451,168,2 170,1,300,101,2 168,110,277,203,2 203,152,293,230,2 388,1,538,76,2 524,221,637,301,2 470,122,553,205,2 511,94,606,201,2 472,290,640,474,0 413,230,482,303,1 68,113,113,157,1 112,25,164,64,1\\n', 'dataset/train_img/BloodImage_00408.jpg 64,279,139,415,2 118,120,232,219,2 1,178,92,277,2 213,304,316,404,2 215,391,313,480,2 454,241,557,335,2 308,178,421,273,2 284,47,392,159,2 470,1,574,68,2 391,44,489,139,2 387,136,480,253,2 542,271,626,363,2 9,28,105,135,2 103,1,201,95,2 198,1,285,81,2 1,281,21,316,1 383,254,424,284,1 310,319,569,480,0\\n', 'dataset/train_img/BloodImage_00409.jpg 518,168,611,268,2 426,87,551,198,2 502,298,611,411,2 250,245,371,357,2 58,104,170,215,2 378,258,504,368,2 228,126,320,217,2 169,79,261,170,2 2,1,93,83,2 16,229,121,313,2 7,263,115,367,2 349,127,456,286,2 438,1,563,77,2 227,71,310,150,2 413,396,456,433,1 334,210,377,248,1 180,169,213,217,1 78,15,122,51,1 78,293,306,480,0\\n', 'dataset/train_img/BloodImage_00410.jpg 233,368,338,452,2 346,385,456,476,2 241,73,351,164,2 250,1,366,85,2 106,315,226,428,2 289,160,394,244,2 350,109,455,193,2 104,20,209,104,2 1,221,108,322,2 582,379,639,480,2 457,372,562,456,2 563,20,640,113,2 304,262,400,387,2 239,275,291,321,1 121,260,189,320,1 57,119,104,167,1 1,286,29,327,1 367,166,611,394,0\\n']\n","nms iou: 0.413 score: 0.3\n","num class :  3\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DfmZzGK9G5_F","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1596072394305,"user_tz":-480,"elapsed":4752,"user":{"displayName":"李智揚","photoUrl":"","userId":"08321859986425279811"}}},"source":["model.yolo_model.load_weights('/content/drive/My Drive/yolo-v4-tf.keras/bccd.h5', by_name=True)"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"id":"uPLeCW8uzEso","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1596072476915,"user_tz":-480,"elapsed":1654,"user":{"displayName":"李智揚","photoUrl":"","userId":"08321859986425279811"}}},"source":["y_true = [\n","    tf.keras.layers.Input(name='input_2', shape=(52, 52, 3, (NUM_CLASS + 5))),  # label_sbbox\n","    tf.keras.layers.Input(name='input_3', shape=(26, 26, 3, (NUM_CLASS + 5))),  # label_mbbox\n","    tf.keras.layers.Input(name='input_4', shape=(13, 13, 3, (NUM_CLASS + 5))),  # label_lbbox\n","    tf.keras.layers.Input(name='input_5', shape=(100, 4)),             # true_bboxes\n","]\n","loss_list = tf.keras.layers.Lambda(yolo_loss, name='yolo_loss',\n","                        arguments={'num_classes': NUM_CLASS, 'iou_loss_thresh': 0.5,\n","                                    'anchors': anchors.reshape((3, 3, 2))})([*model.yolo_model.output, *y_true])\n","model2 = tf.keras.models.Model([model.yolo_model.input, *y_true], loss_list)\n","\n","model2.compile(loss={'yolo_loss': lambda y_true, y_pred: y_pred}, optimizer=tf.keras.optimizers.Adam(lr=1e-3))\n","logs = np.array([])"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"U7lYUkoAzH0c","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"486d3426-8d99-4bc1-cce1-21540b0502cd"},"source":["model2.fit(data_gen, \n","           epochs=1000, initial_epoch=32,\n","            callbacks=[tf.keras.callbacks.ModelCheckpoint('/content/drive/My Drive/bccd.h5', save_best_only=True, save_weights_only=False, monitor='loss')],\n","           )\n","# for epoch in range(50):\n","#     for x_batch, y_batch_tensor, y_batch_bbox in data_gen:\n","#         y_true = [np.zeros(BS)]\n","#         # print('train on batch ', y_batch_tensor[0].shape)\n","#         losses = model2.train_on_batch([x_batch, *y_batch_tensor, y_batch_bbox], y_true)\n","#         loss = losses\n","# #         if len(logs) > 0 and loss < np.min(logs):\n","# #             model.yolo_model.save('/content/drive/My Drive/yolov4-giou.h5')\n","#         logs = np.append(logs, loss)\n","#     print(f'epoch {epoch} losses ' , logs[-1])\n","#         # train_step(x_batch, y_batch_tensor, y_batch_bbox)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 33/1000\n","46/46 [==============================] - 70s 2s/step - loss: 108.7287\n","Epoch 34/1000\n","46/46 [==============================] - 68s 1s/step - loss: 81.3668\n","Epoch 35/1000\n","46/46 [==============================] - 67s 1s/step - loss: 69.4386\n","Epoch 36/1000\n","46/46 [==============================] - 67s 1s/step - loss: 59.3666\n","Epoch 37/1000\n","46/46 [==============================] - 68s 1s/step - loss: 53.3434\n","Epoch 38/1000\n","46/46 [==============================] - 68s 1s/step - loss: 50.6918\n","Epoch 39/1000\n","46/46 [==============================] - 68s 1s/step - loss: 49.2675\n","Epoch 40/1000\n","46/46 [==============================] - 67s 1s/step - loss: 47.6252\n","Epoch 41/1000\n","46/46 [==============================] - 67s 1s/step - loss: 46.9147\n","Epoch 42/1000\n","46/46 [==============================] - 67s 1s/step - loss: 46.5730\n","Epoch 43/1000\n","46/46 [==============================] - 68s 1s/step - loss: 45.5564\n","Epoch 44/1000\n","46/46 [==============================] - 67s 1s/step - loss: 45.1387\n","Epoch 45/1000\n","46/46 [==============================] - 67s 1s/step - loss: 44.2948\n","Epoch 46/1000\n","46/46 [==============================] - 67s 1s/step - loss: 43.4769\n","Epoch 47/1000\n","46/46 [==============================] - 59s 1s/step - loss: 43.5276\n","Epoch 48/1000\n","46/46 [==============================] - 71s 2s/step - loss: 42.0614\n","Epoch 49/1000\n","46/46 [==============================] - 67s 1s/step - loss: 41.9489\n","Epoch 50/1000\n","46/46 [==============================] - 73s 2s/step - loss: 41.4676\n","Epoch 51/1000\n","46/46 [==============================] - 68s 1s/step - loss: 40.9160\n","Epoch 52/1000\n","46/46 [==============================] - 69s 1s/step - loss: 39.6566\n","Epoch 53/1000\n","46/46 [==============================] - 69s 2s/step - loss: 39.5868\n","Epoch 54/1000\n","46/46 [==============================] - 70s 2s/step - loss: 39.1238\n","Epoch 55/1000\n","46/46 [==============================] - 67s 1s/step - loss: 38.3231\n","Epoch 56/1000\n","46/46 [==============================] - 67s 1s/step - loss: 37.7451\n","Epoch 57/1000\n","46/46 [==============================] - 71s 2s/step - loss: 36.3434\n","Epoch 58/1000\n","46/46 [==============================] - 67s 1s/step - loss: 35.9893\n","Epoch 59/1000\n","46/46 [==============================] - 67s 1s/step - loss: 35.3369\n","Epoch 60/1000\n","46/46 [==============================] - 70s 2s/step - loss: 34.0947\n","Epoch 61/1000\n","46/46 [==============================] - 68s 1s/step - loss: 32.9952\n","Epoch 62/1000\n","46/46 [==============================] - 68s 1s/step - loss: 32.5974\n","Epoch 63/1000\n","46/46 [==============================] - 71s 2s/step - loss: 31.3496\n","Epoch 64/1000\n","46/46 [==============================] - 68s 1s/step - loss: 30.7749\n","Epoch 65/1000\n","46/46 [==============================] - 68s 1s/step - loss: 29.1267\n","Epoch 66/1000\n","46/46 [==============================] - 70s 2s/step - loss: 27.8877\n","Epoch 67/1000\n","46/46 [==============================] - 67s 1s/step - loss: 27.0359\n","Epoch 68/1000\n","46/46 [==============================] - 70s 2s/step - loss: 24.9424\n","Epoch 69/1000\n","46/46 [==============================] - 68s 1s/step - loss: 23.8921\n","Epoch 70/1000\n","46/46 [==============================] - 67s 1s/step - loss: 22.8295\n","Epoch 71/1000\n","46/46 [==============================] - 68s 1s/step - loss: 20.3356\n","Epoch 72/1000\n","46/46 [==============================] - 68s 1s/step - loss: 18.9502\n","Epoch 73/1000\n","46/46 [==============================] - 67s 1s/step - loss: 17.6136\n","Epoch 74/1000\n","46/46 [==============================] - 69s 1s/step - loss: 16.4003\n","Epoch 75/1000\n","46/46 [==============================] - 67s 1s/step - loss: 15.4596\n","Epoch 76/1000\n","46/46 [==============================] - 68s 1s/step - loss: 12.2601\n","Epoch 77/1000\n","46/46 [==============================] - 67s 1s/step - loss: 11.4462\n","Epoch 78/1000\n","46/46 [==============================] - 71s 2s/step - loss: 10.7950\n","Epoch 79/1000\n","46/46 [==============================] - 59s 1s/step - loss: 12.2849\n","Epoch 80/1000\n","46/46 [==============================] - 67s 1s/step - loss: 10.3439\n","Epoch 81/1000\n","46/46 [==============================] - 71s 2s/step - loss: 10.3163\n","Epoch 82/1000\n","46/46 [==============================] - 67s 1s/step - loss: 9.5609\n","Epoch 83/1000\n","46/46 [==============================] - 67s 1s/step - loss: 8.2160\n","Epoch 84/1000\n","46/46 [==============================] - 71s 2s/step - loss: 7.1629\n","Epoch 85/1000\n","46/46 [==============================] - 68s 1s/step - loss: 6.7864\n","Epoch 86/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 87/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 88/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 89/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 90/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 91/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 92/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 93/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 94/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 95/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 96/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 97/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 98/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 99/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 100/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 101/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 102/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 103/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 104/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 105/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 106/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 107/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 108/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 109/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 110/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 111/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 112/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 113/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 114/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 115/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 116/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 117/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 118/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 119/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 120/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 121/1000\n","46/46 [==============================] - 59s 1s/step - loss: nan\n","Epoch 122/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 123/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 124/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 125/1000\n","46/46 [==============================] - 59s 1s/step - loss: nan\n","Epoch 126/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 127/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 128/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 129/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 130/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 131/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 132/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 133/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 134/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 135/1000\n","46/46 [==============================] 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nan\n","Epoch 419/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 420/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 421/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 422/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 423/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 424/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 425/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 426/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 427/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 428/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 429/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 430/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 431/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 432/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 433/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 434/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 435/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 436/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 437/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 438/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 439/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 440/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 441/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 442/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 443/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 444/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 445/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 446/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 447/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 448/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 449/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 450/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 451/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 452/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 453/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 454/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 455/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 456/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 457/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 458/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 459/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 460/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 461/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 462/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 463/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 464/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 465/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 466/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 467/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 468/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 469/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 470/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 471/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 472/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 473/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 474/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 475/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 476/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 477/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 478/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 479/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 480/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 481/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 482/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 483/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 484/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 485/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 486/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 487/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 488/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 489/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 490/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 491/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 492/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 493/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 494/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 495/1000\n","46/46 [==============================] - 58s 1s/step - loss: nan\n","Epoch 496/1000\n"," 3/46 [>.............................] - ETA: 37s - loss: nan"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"2psIOo2j3L4N","colab_type":"code","colab":{}},"source":["# model.yolo_model.save('loss125.h5')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bLWvcafSz2SF","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":54},"executionInfo":{"status":"ok","timestamp":1596072493100,"user_tz":-480,"elapsed":11959,"user":{"displayName":"李智揚","photoUrl":"","userId":"08321859986425279811"}},"outputId":"c4c8c2a1-7f03-4789-b123-31c719cc95b1"},"source":["x_batch, y_true = data_gen.__getitem__(0)\n","# y_true = [np.zeros(BS), np.zeros(BS), np.zeros(BS)]\n","# y_true = [np.zeros(BS)]\n","# loss = model2.predict([x_batch, *y_batch_tensor, y_batch_bbox])\n","loss2 = model2.evaluate(x_batch, y_true)\n","print(loss2)"],"execution_count":9,"outputs":[{"output_type":"stream","text":["1/1 [==============================] - 0s 2ms/step - loss: 461.3955\n","461.3955383300781\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"aPjVLKXb0AWH","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":170},"executionInfo":{"status":"error","timestamp":1595999652011,"user_tz":-480,"elapsed":692,"user":{"displayName":"CY Li","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhUjBR-W3fjmzlI1F96TtArIXePd_MCbM8FFhB5fbI=s64","userId":"11560903483017836470"}},"outputId":"2775fa73-022b-41e8-e544-8b40bf84f4ec"},"source":["model.predict('/content/drive/My Drive/yolo-v4-tf.keras/dataset/train_img2/test3.jpg')"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-05d095fe2128>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/My Drive/yolo-v4-tf.keras/dataset/train_img2/test3.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"]}]},{"cell_type":"code","metadata":{"id":"329jjLqQ1eN0","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":845},"executionInfo":{"status":"ok","timestamp":1596072513413,"user_tz":-480,"elapsed":4455,"user":{"displayName":"李智揚","photoUrl":"","userId":"08321859986425279811"}},"outputId":"b55f8521-297b-4c02-dffc-4e886d8e69fb"},"source":["i = np.random.randint(len(lines))\n","path = '/content/drive/My Drive/yolo-v4-tf.keras/' + lines[i].split(' ')[0] # f'/content/drive/My Drive/yolo-v4-tf.keras/dataset/train_img/BloodImage_00372.jpg'\n","print(path)\n","# model.predict(path)\n","model.predict_nonms(path, iou_threshold=0.6, score_threshold=0.1)"],"execution_count":10,"outputs":[{"output_type":"stream","text":["/content/drive/My Drive/yolo-v4-tf.keras/dataset/train_img/BloodImage_00018.jpg\n","img shape:  (480, 640, 3)\n","nms iou: 0.6 score: 0.1\n","# of bboxes: 16\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 360x360 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>x1</th>\n","      <th>y1</th>\n","      <th>x2</th>\n","      <th>y2</th>\n","      <th>class_name</th>\n","      <th>score</th>\n","      <th>w</th>\n","      <th>h</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>39</td>\n","      <td>206</td>\n","      <td>269</td>\n","      <td>444</td>\n","      <td>WBC</td>\n","      <td>0.964788</td>\n","      <td>230</td>\n","      <td>238</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>178</td>\n","      <td>78</td>\n","      <td>296</td>\n","      <td>201</td>\n","      <td>RBC</td>\n","      <td>0.938469</td>\n","      <td>118</td>\n","      <td>123</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>124</td>\n","      <td>84</td>\n","      <td>RBC</td>\n","      <td>0.869789</td>\n","      <td>124</td>\n","      <td>84</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>29</td>\n","      <td>130</td>\n","      <td>172</td>\n","      <td>WBC</td>\n","      <td>0.787741</td>\n","      <td>130</td>\n","      <td>143</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>463</td>\n","      <td>210</td>\n","      <td>574</td>\n","      <td>299</td>\n","      <td>RBC</td>\n","      <td>0.566140</td>\n","      <td>111</td>\n","      <td>89</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>495</td>\n","      <td>255</td>\n","      <td>614</td>\n","      <td>388</td>\n","      <td>WBC</td>\n","      <td>0.479953</td>\n","      <td>119</td>\n","      <td>133</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>578</td>\n","      <td>200</td>\n","      <td>627</td>\n","      <td>273</td>\n","      <td>Platelets</td>\n","      <td>0.462415</td>\n","      <td>49</td>\n","      <td>73</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>127</td>\n","      <td>8</td>\n","      <td>238</td>\n","      <td>132</td>\n","      <td>RBC</td>\n","      <td>0.282668</td>\n","      <td>111</td>\n","      <td>124</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>402</td>\n","      <td>416</td>\n","      <td>507</td>\n","      <td>480</td>\n","      <td>Platelets</td>\n","      <td>0.248275</td>\n","      <td>105</td>\n","      <td>64</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>297</td>\n","      <td>6</td>\n","      <td>434</td>\n","      <td>135</td>\n","      <td>RBC</td>\n","      <td>0.235680</td>\n","      <td>137</td>\n","      <td>129</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>499</td>\n","      <td>117</td>\n","      <td>557</td>\n","      <td>185</td>\n","      <td>RBC</td>\n","      <td>0.224382</td>\n","      <td>58</td>\n","      <td>68</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>573</td>\n","      <td>77</td>\n","      <td>637</td>\n","      <td>172</td>\n","      <td>Platelets</td>\n","      <td>0.212073</td>\n","      <td>64</td>\n","      <td>95</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>578</td>\n","      <td>200</td>\n","      <td>627</td>\n","      <td>273</td>\n","      <td>RBC</td>\n","      <td>0.195277</td>\n","      <td>49</td>\n","      <td>73</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>402</td>\n","      <td>416</td>\n","      <td>507</td>\n","      <td>480</td>\n","      <td>RBC</td>\n","      <td>0.193484</td>\n","      <td>105</td>\n","      <td>64</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>292</td>\n","      <td>236</td>\n","      <td>420</td>\n","      <td>375</td>\n","      <td>WBC</td>\n","      <td>0.161821</td>\n","      <td>128</td>\n","      <td>139</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>402</td>\n","      <td>416</td>\n","      <td>507</td>\n","      <td>480</td>\n","      <td>WBC</td>\n","      <td>0.141249</td>\n","      <td>105</td>\n","      <td>64</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     x1   y1   x2   y2 class_name     score    w    h\n","0    39  206  269  444        WBC  0.964788  230  238\n","1   178   78  296  201        RBC  0.938469  118  123\n","2     0    0  124   84        RBC  0.869789  124   84\n","3     0   29  130  172        WBC  0.787741  130  143\n","4   463  210  574  299        RBC  0.566140  111   89\n","5   495  255  614  388        WBC  0.479953  119  133\n","6   578  200  627  273  Platelets  0.462415   49   73\n","7   127    8  238  132        RBC  0.282668  111  124\n","8   402  416  507  480  Platelets  0.248275  105   64\n","9   297    6  434  135        RBC  0.235680  137  129\n","10  499  117  557  185        RBC  0.224382   58   68\n","11  573   77  637  172  Platelets  0.212073   64   95\n","12  578  200  627  273        RBC  0.195277   49   73\n","13  402  416  507  480        RBC  0.193484  105   64\n","14  292  236  420  375        WBC  0.161821  128  139\n","15  402  416  507  480        WBC  0.141249  105   64"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"QAvKXwc3lp2f","colab_type":"code","colab":{}},"source":["#!/usr/bin/env python\n","# coding: utf-8\n","\n","\n","\n","import cv2\n","import numpy as np\n","from utils import DataGenerator, preprocess_true_boxes\n","import matplotlib.pyplot as plt\n","import tensorflow.keras.backend as K\n","import tensorflow as tf\n","import math\n","\n","from models import Yolov4, yolov4_head, get_boxes\n","from config import yolo_config\n","\n","print(tf.__version__)\n","# In[3]:\n","\n","\n","# with open('/content/drive/My Drive/yolo-v4-tf.keras/dataset/train_txt/anno2.txt') as f:\n","with open('/content/drive/My Drive/yolo-v4-tf.keras/dataset/train_txt/anno.txt') as f:\n","    lines = f.readlines()\n","lines = lines[:1]\n","# lines = lines * 8\n","print(lines)\n","# lines = lines * 32\n","\n","# In[4]:\n","\n","NUM_CLASS = 80\n","FOLDER_PATH = '/content/drive/My Drive/yolo-v4-tf.keras'\n","BS = 1\n","anchors = np.array([12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401]).reshape((-1, 2))\n","\n","\n","# In[6]:\n","\n","\n","data_gen = DataGenerator(lines[:], BS, (416, 416), num_classes=NUM_CLASS, folder_path=FOLDER_PATH, anchors=anchors)\n","\n","\n","\n","model = Yolov4(\n","                weight_path=None,\n","                # class_name_path='/content/drive/My Drive/yolo-v4-tf.keras/bccd_classes.txt'\n","               class_name_path='/content/drive/My Drive/yolo-v4-tf.keras/coco_classes.txt',\n","#               weight_path='yolov4.weights',\n","\n","#                img_size=(416, 416, 3),\n","            \n","              )\n","model.build_model(load_pretrained=False)\n","# model.load_model('/content/drive/My Drive/yolov4-giou.h5')\n","print('num class : ', model.num_classes)\n","\n","\n","\n","# In[29]:\n","\n","\n","# from tflite yolov4\n","def bbox_giou(bboxes1, bboxes2):\n","    \"\"\"\n","    Generalized IoU\n","    @param bboxes1: (a, b, ..., 4)\n","    @param bboxes2: (A, B, ..., 4)\n","        x:X is 1:n or n:n or n:1\n","    @return (max(a,A), max(b,B), ...)\n","    ex) (4,):(3,4) -> (3,)\n","        (2,1,4):(2,3,4) -> (2,3)\n","    \"\"\"\n","    bboxes1_area = bboxes1[..., 2] * bboxes1[..., 3]\n","    bboxes2_area = bboxes2[..., 2] * bboxes2[..., 3]\n","\n","    bboxes1_coor = tf.concat(\n","        [\n","            bboxes1[..., :2] - bboxes1[..., 2:] * 0.5,\n","            bboxes1[..., :2] + bboxes1[..., 2:] * 0.5,\n","        ],\n","        axis=-1,\n","    )\n","    bboxes2_coor = tf.concat(\n","        [\n","            bboxes2[..., :2] - bboxes2[..., 2:] * 0.5,\n","            bboxes2[..., :2] + bboxes2[..., 2:] * 0.5,\n","        ],\n","        axis=-1,\n","    )\n","\n","    left_up = tf.maximum(bboxes1_coor[..., :2], bboxes2_coor[..., :2])\n","    right_down = tf.minimum(bboxes1_coor[..., 2:], bboxes2_coor[..., 2:])\n","\n","    inter_section = tf.maximum(right_down - left_up, 0.0)\n","    inter_area = inter_section[..., 0] * inter_section[..., 1]\n","\n","    union_area = bboxes1_area + bboxes2_area - inter_area\n","\n","    iou = tf.math.divide_no_nan(inter_area, union_area)\n","\n","    enclose_left_up = tf.minimum(bboxes1_coor[..., :2], bboxes2_coor[..., :2])\n","    enclose_right_down = tf.maximum(\n","        bboxes1_coor[..., 2:], bboxes2_coor[..., 2:]\n","    )\n","\n","    enclose_section = enclose_right_down - enclose_left_up\n","    enclose_area = enclose_section[..., 0] * enclose_section[..., 1]\n","\n","    giou = iou - tf.math.divide_no_nan(enclose_area - union_area, enclose_area)\n","\n","    return giou\n","def bbox_ciou(boxes1, boxes2):\n","    '''\n","    计算ciou = iou - p2/c2 - av\n","    :param boxes1: (8, 13, 13, 3, 4)   pred_xywh\n","    :param boxes2: (8, 13, 13, 3, 4)   label_xywh\n","    :return:\n","\n","    举例时假设pred_xywh和label_xywh的shape都是(1, 4)\n","    '''\n","\n","    # 变成左上角坐标、右下角坐标\n","    boxes1_x0y0x1y1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,\n","                                 boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)\n","    boxes2_x0y0x1y1 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,\n","                                 boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)\n","    '''\n","    逐个位置比较boxes1_x0y0x1y1[..., :2]和boxes1_x0y0x1y1[..., 2:]，即逐个位置比较[x0, y0]和[x1, y1]，小的留下。\n","    比如留下了[x0, y0]\n","    这一步是为了避免一开始w h 是负数，导致x0y0成了右下角坐标，x1y1成了左上角坐标。\n","    '''\n","    boxes1_x0y0x1y1 = tf.concat([tf.minimum(boxes1_x0y0x1y1[..., :2], boxes1_x0y0x1y1[..., 2:]),\n","                                 tf.maximum(boxes1_x0y0x1y1[..., :2], boxes1_x0y0x1y1[..., 2:])], axis=-1)\n","    boxes2_x0y0x1y1 = tf.concat([tf.minimum(boxes2_x0y0x1y1[..., :2], boxes2_x0y0x1y1[..., 2:]),\n","                                 tf.maximum(boxes2_x0y0x1y1[..., :2], boxes2_x0y0x1y1[..., 2:])], axis=-1)\n","\n","    # 两个矩形的面积\n","    boxes1_area = (boxes1_x0y0x1y1[..., 2] - boxes1_x0y0x1y1[..., 0]) * (\n","                boxes1_x0y0x1y1[..., 3] - boxes1_x0y0x1y1[..., 1])\n","    boxes2_area = (boxes2_x0y0x1y1[..., 2] - boxes2_x0y0x1y1[..., 0]) * (\n","                boxes2_x0y0x1y1[..., 3] - boxes2_x0y0x1y1[..., 1])\n","\n","    # 相交矩形的左上角坐标、右下角坐标，shape 都是 (8, 13, 13, 3, 2)\n","    left_up = tf.maximum(boxes1_x0y0x1y1[..., :2], boxes2_x0y0x1y1[..., :2])\n","    right_down = tf.minimum(boxes1_x0y0x1y1[..., 2:], boxes2_x0y0x1y1[..., 2:])\n","\n","    # 相交矩形的面积inter_area。iou\n","    inter_section = tf.maximum(right_down - left_up, 0.0)\n","    inter_area = inter_section[..., 0] * inter_section[..., 1]\n","    union_area = boxes1_area + boxes2_area - inter_area\n","    iou = inter_area / (union_area + 1e-9)\n","\n","    # 包围矩形的左上角坐标、右下角坐标，shape 都是 (8, 13, 13, 3, 2)\n","    enclose_left_up = tf.minimum(boxes1_x0y0x1y1[..., :2], boxes2_x0y0x1y1[..., :2])\n","    enclose_right_down = tf.maximum(boxes1_x0y0x1y1[..., 2:], boxes2_x0y0x1y1[..., 2:])\n","\n","    # 包围矩形的对角线的平方\n","    enclose_wh = enclose_right_down - enclose_left_up\n","    enclose_c2 = K.pow(enclose_wh[..., 0], 2) + K.pow(enclose_wh[..., 1], 2)\n","\n","    # 两矩形中心点距离的平方\n","    p2 = K.pow(boxes1[..., 0] - boxes2[..., 0], 2) + K.pow(boxes1[..., 1] - boxes2[..., 1], 2)\n","\n","    # 增加av。加上除0保护防止nan。\n","    atan1 = tf.atan(boxes1[..., 2] / (boxes1[..., 3] + 1e-9))\n","    atan2 = tf.atan(boxes2[..., 2] / (boxes2[..., 3] + 1e-9))\n","    v = 4.0 * K.pow(atan1 - atan2, 2) / (math.pi ** 2)\n","    a = v / (1 - iou + v)\n","\n","    ciou = iou - 1.0 * p2 / enclose_c2 - 1.0 * a * v\n","    return ciou\n","def bbox_iou(boxes1, boxes2):\n","    boxes1_area = boxes1[..., 2] * boxes1[..., 3]  # 所有格子的3个预测框的面积\n","    boxes2_area = boxes2[..., 2] * boxes2[..., 3]  # 所有ground truth的面积\n","\n","    # (x, y, w, h)变成(x0, y0, x1, y1)\n","    boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5,\n","                        boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)\n","    boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5,\n","                        boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)\n","\n","    # 所有格子的3个预测框 分别 和  70个ground truth  计算iou。 所以left_up和right_down的shape = (?, grid_h, grid_w, 3, 70, 2)\n","    left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2])  # 相交矩形的左上角坐标\n","    right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])  # 相交矩形的右下角坐标\n","\n","    inter_section = tf.maximum(right_down - left_up, 0.0)  # 相交矩形的w和h，是负数时取0     (?, grid_h, grid_w, 3, 70, 2)\n","    inter_area = inter_section[..., 0] * inter_section[..., 1]  # 相交矩形的面积            (?, grid_h, grid_w, 3, 70)\n","    union_area = boxes1_area + boxes2_area - inter_area  # union_area      (?, grid_h, grid_w, 3, 70)\n","    iou = 1.0 * inter_area / (union_area + 1e-9)  # iou                             (?, grid_h, grid_w, 3, 70)\n","    return iou\n","\n","def yolo_loss_wrapper(input_shape, STRIDES, NUM_CLASS, ANCHORS, XYSCALES, IOU_LOSS_THRESH):\n","    input_shape = input_shape[0]\n","    def yolo_loss(label, y_pred, y_batch_box):\n","        bboxes = decode_train2(y_pred, input_shape // STRIDES, NUM_CLASS, STRIDES, ANCHORS, XYSCALES)\n","        \n","        conv_shape = tf.shape(y_pred)\n","        batch_size = conv_shape[0]\n","        output_size = conv_shape[1]\n","        input_size = STRIDES * output_size\n","        # conv = tf.reshape(conv, (batch_size, output_size, output_size,\n","        #                          3, 5 + num_class))\n","        conv_raw_prob = bboxes[:, :, :, :, 5:]\n","\n","        pred_xywh = bboxes[:, :, :, :, 0:4]\n","        pred_conf = bboxes[:, :, :, :, 4:5]\n","\n","        label_xywh = label[:, :, :, :, 0:4]\n","        respond_bbox = label[:, :, :, :, 4:5]\n","        label_prob = label[:, :, :, :, 5:]\n","\n","        ciou = tf.expand_dims(bbox_giou(pred_xywh, label_xywh), axis=-1)  # (8, 13, 13, 3, 1)\n","        # ciou = tf.expand_dims(bbox_ciou(pred_xywh, label_xywh), axis=-1)  # (8, 13, 13, 3, 1)\n","        input_size = tf.cast(input_size, tf.float32)\n","\n","        # 每个预测框xxxiou_loss的权重 = 2 - (ground truth的面积/图片面积)\n","        bbox_loss_scale = 2.0 - 1.0 * label_xywh[:, :, :, :, 2:3] * label_xywh[:, :, :, :, 3:4] / (input_size ** 2)\n","        ciou_loss = respond_bbox * bbox_loss_scale * (1 - ciou)  # 1. respond_bbox作为mask，有物体才计算xxxiou_loss\n","\n","        # 2. respond_bbox作为mask，有物体才计算类别loss\n","        prob_loss = respond_bbox *  tf.nn.sigmoid_cross_entropy_with_logits(label_prob, conv_raw_prob)\n","        # 等价于\n","        # pred_prob = pred[:, :, :, :, 5:]\n","        # prob_pos_loss = label_prob * (0 - K.log(pred_prob + 1e-9))\n","        # prob_neg_loss = (1 - label_prob) * (0 - K.log(1 - pred_prob + 1e-9))\n","        # prob_mask = tf.tile(respond_bbox, [1, 1, 1, 1, num_class])\n","        # prob_loss = prob_mask * (prob_pos_loss + prob_neg_loss)\n","\n","        # 3. xxxiou_loss和类别loss比较简单。重要的是conf_loss，是一个二值交叉熵损失\n","        # 分两步：第一步是确定 grid_h * grid_w * 3 个预测框 哪些作为反例；第二步是计算二值交叉熵损失。\n","        expand_pred_xywh = pred_xywh[:, :, :, :, np.newaxis, :]  # 扩展为(?, grid_h, grid_w, 3,   1, 4)\n","        expand_bboxes = y_batch_bbox[:, np.newaxis, np.newaxis, np.newaxis, :, :]  # 扩展为(?,      1,      1, 1, 70, 4)\n","        iou = bbox_iou(expand_pred_xywh,\n","                       expand_bboxes)  # 所有格子的3个预测框 分别 和  70个ground truth  计算iou。   (?, grid_h, grid_w, 3, 70)\n","        max_iou = tf.expand_dims(tf.reduce_max(iou, axis=-1),\n","                                 axis=-1)  # 与70个ground truth的iou中，保留最大那个iou。  (?, grid_h, grid_w, 3, 1)\n","\n","        # respond_bgd代表  这个分支输出的 grid_h * grid_w * 3 个预测框是否是 反例（背景）\n","        # label有物体，respond_bgd是0。 没物体的话：如果和某个gt(共70个)的iou超过iou_loss_thresh，respond_bgd是0；如果和所有gt(最多70个)的iou都小于iou_loss_thresh，respond_bgd是1。\n","        # respond_bgd是0代表有物体，不是反例（或者是忽略框）；  权重respond_bgd是1代表没有物体，是反例。\n","        # 有趣的是，模型训练时由于不断更新，对于同一张图片，两次预测的 grid_h * grid_w * 3 个预测框（对于这个分支输出）  是不同的。用的是这些预测框来与gt计算iou来确定哪些预测框是反例。\n","        # 而不是用固定大小（不固定位置）的先验框。\n","        respond_bgd = (1.0 - respond_bbox) * tf.cast(max_iou < IOU_LOSS_THRESH, tf.float32)\n","\n","        # 二值交叉熵损失\n","        pos_loss = respond_bbox * (0 - K.log(pred_conf + 1e-9))\n","        neg_loss = respond_bgd * (0 - K.log(1 - pred_conf + 1e-9))\n","\n","        conf_loss = pos_loss + neg_loss\n","        # 回顾respond_bgd，某个预测框和某个gt的iou超过iou_loss_thresh，不被当作是反例。在参与“预测的置信位 和 真实置信位 的 二值交叉熵”时，这个框也可能不是正例(label里没标这个框是1的话)。这个框有可能不参与置信度loss的计算。\n","        # 这种框一般是gt框附近的框，或者是gt框所在格子的另外两个框。它既不是正例也不是反例不参与置信度loss的计算。（论文里称之为ignore）\n","\n","        ciou_loss = tf.reduce_mean(tf.reduce_sum(ciou_loss, axis=[1, 2, 3, 4]))  # 每个样本单独计算自己的ciou_loss，再求平均值\n","        conf_loss = tf.reduce_mean(tf.reduce_sum(conf_loss, axis=[1, 2, 3, 4]))  # 每个样本单独计算自己的conf_loss，再求平均值\n","        prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1, 2, 3, 4]))  # 每个样本单独计算自己的prob_loss，再求平均值\n","\n","        return ciou_loss, conf_loss, prob_loss    \n","\n","    return yolo_loss\n","def decode(conv_output, anchors, stride, num_class):\n","    conv_shape       = tf.shape(conv_output)\n","    batch_size       = conv_shape[0]\n","    output_size      = conv_shape[1]\n","    anchor_per_scale = len(anchors)\n","    conv_output = tf.reshape(conv_output, (batch_size, output_size, output_size, anchor_per_scale, 5 + num_class))\n","    conv_raw_dxdy = conv_output[:, :, :, :, 0:2]\n","    conv_raw_dwdh = conv_output[:, :, :, :, 2:4]\n","    conv_raw_conf = conv_output[:, :, :, :, 4:5]\n","    conv_raw_prob = conv_output[:, :, :, :, 5: ]\n","    y = tf.tile(tf.range(output_size, dtype=tf.int32)[:, tf.newaxis], [1, output_size])\n","    x = tf.tile(tf.range(output_size, dtype=tf.int32)[tf.newaxis, :], [output_size, 1])\n","    xy_grid = tf.concat([x[:, :, tf.newaxis], y[:, :, tf.newaxis]], axis=-1)\n","    xy_grid = tf.tile(xy_grid[tf.newaxis, :, :, tf.newaxis, :], [batch_size, 1, 1, anchor_per_scale, 1])\n","    xy_grid = tf.cast(xy_grid, tf.float32)\n","    pred_xy = (tf.sigmoid(conv_raw_dxdy) + xy_grid) * stride\n","    pred_wh = (tf.exp(conv_raw_dwdh) * anchors)\n","    pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1)\n","    pred_conf = tf.sigmoid(conv_raw_conf)\n","    pred_prob = tf.sigmoid(conv_raw_prob)\n","    return tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1)\n","def loss_layer(conv, pred, label, bboxes, stride, num_class, iou_loss_thresh):\n","    conv_shape = tf.shape(conv)\n","    batch_size = conv_shape[0]\n","    output_size = conv_shape[1]\n","    input_size = stride * output_size\n","    conv = tf.reshape(conv, (batch_size, output_size, output_size,\n","                             3, 5 + num_class))\n","    conv_raw_prob = conv[:, :, :, :, 5:]\n","\n","    pred_xywh = pred[:, :, :, :, 0:4]\n","    pred_conf = pred[:, :, :, :, 4:5]\n","\n","    label_xywh = label[:, :, :, :, 0:4]\n","    respond_bbox = label[:, :, :, :, 4:5]\n","    label_prob = label[:, :, :, :, 5:]\n","\n","    ciou = tf.expand_dims(bbox_ciou(pred_xywh, label_xywh), axis=-1)  # (8, 13, 13, 3, 1)\n","    input_size = tf.cast(input_size, tf.float32)\n","\n","    # 每个预测框xxxiou_loss的权重 = 2 - (ground truth的面积/图片面积)\n","    bbox_loss_scale = 2.0 - 1.0 * label_xywh[:, :, :, :, 2:3] * label_xywh[:, :, :, :, 3:4] / (input_size ** 2)\n","    ciou_loss = respond_bbox * bbox_loss_scale * (1 - ciou)  # 1. respond_bbox作为mask，有物体才计算xxxiou_loss\n","\n","    # 2. respond_bbox作为mask，有物体才计算类别loss\n","    prob_loss = respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=label_prob, logits=conv_raw_prob)\n","    # 等价于\n","    # pred_prob = pred[:, :, :, :, 5:]\n","    # prob_pos_loss = label_prob * (0 - K.log(pred_prob + 1e-9))\n","    # prob_neg_loss = (1 - label_prob) * (0 - K.log(1 - pred_prob + 1e-9))\n","    # prob_mask = tf.tile(respond_bbox, [1, 1, 1, 1, num_class])\n","    # prob_loss = prob_mask * (prob_pos_loss + prob_neg_loss)\n","\n","\n","    # 3. xxxiou_loss和类别loss比较简单。重要的是conf_loss，是一个二值交叉熵损失\n","    # 分两步：第一步是确定 grid_h * grid_w * 3 个预测框 哪些作为反例；第二步是计算二值交叉熵损失。\n","    expand_pred_xywh = pred_xywh[:, :, :, :, np.newaxis, :]  # 扩展为(?, grid_h, grid_w, 3,   1, 4)\n","    expand_bboxes = bboxes[:, np.newaxis, np.newaxis, np.newaxis, :, :]  # 扩展为(?,      1,      1, 1, 70, 4)\n","    iou = bbox_iou(expand_pred_xywh, expand_bboxes)  # 所有格子的3个预测框 分别 和  70个ground truth  计算iou。   (?, grid_h, grid_w, 3, 70)\n","    max_iou = tf.expand_dims(tf.reduce_max(iou, axis=-1), axis=-1)  # 与70个ground truth的iou中，保留最大那个iou。  (?, grid_h, grid_w, 3, 1)\n","\n","    # respond_bgd代表  这个分支输出的 grid_h * grid_w * 3 个预测框是否是 反例（背景）\n","    # label有物体，respond_bgd是0。 没物体的话：如果和某个gt(共70个)的iou超过iou_loss_thresh，respond_bgd是0；如果和所有gt(最多70个)的iou都小于iou_loss_thresh，respond_bgd是1。\n","    # respond_bgd是0代表有物体，不是反例（或者是忽略框）；  权重respond_bgd是1代表没有物体，是反例。\n","    # 有趣的是，模型训练时由于不断更新，对于同一张图片，两次预测的 grid_h * grid_w * 3 个预测框（对于这个分支输出）  是不同的。用的是这些预测框来与gt计算iou来确定哪些预测框是反例。\n","    # 而不是用固定大小（不固定位置）的先验框。\n","    # respond_bgd = (1.0 - respond_bbox) * tf.cast(max_iou < iou_loss_thresh, tf.float32)\n","    respond_bgd = (1.0 - respond_bbox)\n","\n","    # 二值交叉熵损失\n","    pos_loss = respond_bbox * (0 - K.log(pred_conf + 1e-9))\n","    neg_loss = respond_bgd  * (0 - K.log(1 - pred_conf + 1e-9))\n","\n","    conf_loss = pos_loss + neg_loss\n","    # 回顾respond_bgd，某个预测框和某个gt的iou超过iou_loss_thresh，不被当作是反例。在参与“预测的置信位 和 真实置信位 的 二值交叉熵”时，这个框也可能不是正例(label里没标这个框是1的话)。这个框有可能不参与置信度loss的计算。\n","    # 这种框一般是gt框附近的框，或者是gt框所在格子的另外两个框。它既不是正例也不是反例不参与置信度loss的计算。（论文里称之为ignore）\n","\n","    ciou_loss = tf.reduce_mean(tf.reduce_sum(ciou_loss, axis=[1, 2, 3, 4]))  # 每个样本单独计算自己的ciou_loss，再求平均值\n","    conf_loss = tf.reduce_mean(tf.reduce_sum(conf_loss, axis=[1, 2, 3, 4]))  # 每个样本单独计算自己的conf_loss，再求平均值\n","    prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1, 2, 3, 4]))  # 每个样本单独计算自己的prob_loss，再求平均值\n","\n","    return ciou_loss, conf_loss, prob_loss\n","\n","def decode_train2(conv_output, output_size, NUM_CLASS, STRIDES, ANCHORS, XYSCALE):\n","    conv_output = tf.reshape(conv_output,\n","                             (tf.shape(conv_output)[0], output_size, output_size, 3, 5 + NUM_CLASS))\n","\n","    # conv_raw_dxdy, conv_raw_dwdh, conv_raw_conf, conv_raw_prob = tf.split(conv_output, (2, 2, 1, NUM_CLASS),\n","    #                                                                       axis=-1)\n","    conv_raw_dxdy = conv_output[:, :, :, :, 0:2]\n","    conv_raw_dwdh = conv_output[:, :, :, :, 2:4]\n","    conv_raw_conf = conv_output[:, :, :, :, 4:5]\n","    conv_raw_prob = conv_output[:, :, :, :, 5:]\n","\n","    # xy_grid = tf.meshgrid(tf.range(output_size), tf.range(output_size))\n","    # xy_grid = tf.expand_dims(tf.stack(xy_grid, axis=-1), axis=2)  # [gx, gy, 1, 2]\n","    # xy_grid = tf.tile(tf.expand_dims(xy_grid, axis=0), [tf.shape(conv_output)[0], 1, 1, 3, 1])\n","    # xy_grid = tf.cast(xy_grid, tf.float32)\n","    y = tf.tile(tf.range(output_size, dtype=tf.int32)[:, tf.newaxis], [1, output_size])\n","    x = tf.tile(tf.range(output_size, dtype=tf.int32)[tf.newaxis, :], [output_size, 1])\n","    xy_grid = tf.concat([x[:, :, tf.newaxis], y[:, :, tf.newaxis]], axis=-1)\n","    xy_grid = tf.tile(xy_grid[tf.newaxis, :, :, tf.newaxis, :], [tf.shape(conv_output)[0], 1, 1, 3, 1])\n","    xy_grid = tf.cast(xy_grid, tf.float32)\n","\n","    # pred_xy = ((tf.sigmoid(conv_raw_dxdy) * XYSCALE) - 0.5 * (XYSCALE - 1) + xy_grid) * STRIDES\n","    pred_xy = (tf.sigmoid(conv_raw_dxdy) + xy_grid) * STRIDES\n","    pred_wh = (tf.exp(conv_raw_dwdh) * ANCHORS)\n","    pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1)\n","\n","    pred_conf = tf.sigmoid(conv_raw_conf)\n","    # pred_prob = tf.sigmoid(conv_raw_prob)\n","\n","    return tf.concat([pred_xywh, pred_conf, conv_raw_prob], axis=-1)\n","\n","# x_batch, y_batch = X, y = data_gen.__getitem__(0)\n","\n","\n","losses = [yolo_loss_wrapper(input_shape=(416, 416), \n","                  STRIDES=[8, 16, 32][i], \n","                  NUM_CLASS=NUM_CLASS,\n","                  ANCHORS=anchors.reshape(3, 3, 2)[i], \n","                  XYSCALES=[1., 1., 1.][i], \n","                  IOU_LOSS_THRESH=0.5) for i in range(3)]\n","def yolo_loss2(args, num_classes, iou_loss_thresh, anchors):\n","    conv_lbbox = args[2]   # (?, ?, ?, 3*(num_classes+5))\n","    conv_mbbox = args[1]   # (?, ?, ?, 3*(num_classes+5))\n","    conv_sbbox = args[0]   # (?, ?, ?, 3*(num_classes+5))\n","    label_sbbox = args[3]   # (?, ?, ?, 3, num_classes+5)\n","    label_mbbox = args[4]   # (?, ?, ?, 3, num_classes+5)\n","    label_lbbox = args[5]   # (?, ?, ?, 3, num_classes+5)\n","    true_bboxes = args[6]   # (?, 50, 4)\n","    pred_sbbox = decode(conv_sbbox, anchors[0], 8, num_classes)\n","    pred_mbbox = decode(conv_mbbox, anchors[1], 16, num_classes)\n","    pred_lbbox = decode(conv_lbbox, anchors[2], 32, num_classes)\n","    # pred_sbbox = decode_train2(conv_sbbox, 52, num_classes, 8, anchors[0], 1) # decode(conv_sbbox, anchors[0], 8, num_classes)\n","    # pred_mbbox = decode_train2(conv_mbbox, 26, num_classes, 16, anchors[1], 1) # decode(conv_mbbox, anchors[1], 16, num_classes)\n","    # pred_lbbox = decode_train2(conv_lbbox, 13, num_classes, 32, anchors[2], 1) # decode(conv_lbbox, anchors[2], 32, num_classes)\n","    sbbox_ciou_loss, sbbox_conf_loss, sbbox_prob_loss = loss_layer(conv_sbbox, pred_sbbox, label_sbbox, true_bboxes, 8, num_classes, iou_loss_thresh)\n","    mbbox_ciou_loss, mbbox_conf_loss, mbbox_prob_loss = loss_layer(conv_mbbox, pred_mbbox, label_mbbox, true_bboxes, 16, num_classes, iou_loss_thresh)\n","    lbbox_ciou_loss, lbbox_conf_loss, lbbox_prob_loss = loss_layer(conv_lbbox, pred_lbbox, label_lbbox, true_bboxes, 32, num_classes, iou_loss_thresh)\n","\n","    ciou_loss = lbbox_ciou_loss + sbbox_ciou_loss + mbbox_ciou_loss\n","    conf_loss = lbbox_conf_loss + sbbox_conf_loss + mbbox_conf_loss\n","    prob_loss = lbbox_prob_loss + sbbox_prob_loss + mbbox_prob_loss\n","    # print(ciou_loss, conf_loss, prob_loss)\n","    return ciou_loss, conf_loss, prob_loss\n","\n","# # In[31]:\n","INIT_LR = 1e-4 # 1e-6\n","FINAL_LR = 1e-4 # 1e-8\n","opt = tf.keras.optimizers.Adam(lr=1e-4)\n","opt2 = tf.keras.optimizers.Adam(lr=1e-4)\n","opt3 = tf.keras.optimizers.Adam(lr=1e-4)\n","steps_per_epoch = len(lines) // BS\n","warmup_epochs = 20\n","warmup_steps = warmup_epochs * steps_per_epoch\n","global_steps = 0\n","first_stage_epoch = 200\n","second_stage_epoch = 300\n","total_steps = (first_stage_epoch + second_stage_epoch) * steps_per_epoch\n","\n","y_true = [\n","    tf.keras.layers.Input(name='input_2', shape=(52, 52, 3, (NUM_CLASS + 5))),  # label_sbbox\n","    tf.keras.layers.Input(name='input_3', shape=(26, 26, 3, (NUM_CLASS + 5))),  # label_mbbox\n","    tf.keras.layers.Input(name='input_4', shape=(13, 13, 3, (NUM_CLASS + 5))),  # label_lbbox\n","    tf.keras.layers.Input(name='input_5', shape=(100, 4)),             # true_bboxes\n","]\n","loss_list = tf.keras.layers.Lambda(yolo_loss2, name='yolo_loss',\n","                        arguments={'num_classes': NUM_CLASS, 'iou_loss_thresh': 0.5,\n","                                    'anchors': anchors.reshape((3, 3, 2))})([*model.yolo_model.output, *y_true])\n","model2 = tf.keras.models.Model([model.yolo_model.input, *y_true], loss_list)\n","\n","model2.compile(loss={'yolo_loss': lambda y_true, y_pred: y_pred}, optimizer=tf.keras.optimizers.Adam(lr=1e-4))\n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"gRGaSIyn_MWf","colab_type":"code","colab":{}},"source":["def train_step(x_batch, y_batch_tensor, y_batch_bbox):\n","    with tf.GradientTape() as tape_giou, tf.GradientTape() as tape_conf, tf.GradientTape() as tape_prob:\n","        predict = model.yolo_model(x_batch)\n","        # total_giou_loss = 0\n","        # total_conf_loss = 0\n","        # total_prob_loss = 0\n","        # for i in range(0,3):\n","        #     loss_func = losses[i]\n","        #     giou_loss, conf_loss, prob_loss = loss_func(y_batch_tensor[i], predict[i], y_batch_bbox)\n","        #     total_giou_loss += giou_loss\n","        #     total_conf_loss += conf_loss\n","        #     total_prob_loss += prob_loss\n","        #     print(i, total_giou_loss, total_conf_loss, total_prob_loss)\n","\n","        giou_loss0, conf_loss0, prob_loss0 = losses[0](y_batch_tensor[0], predict[0], y_batch_bbox)\n","        giou_loss1, conf_loss1, prob_loss1 = losses[1](y_batch_tensor[1], predict[1], y_batch_bbox)\n","        giou_loss2, conf_loss2, prob_loss2 = losses[2](y_batch_tensor[2], predict[2], y_batch_bbox)\n","        total_giou_loss = giou_loss0 + giou_loss1 + giou_loss2\n","        total_conf_loss = conf_loss0 + conf_loss1 + conf_loss2\n","        total_prob_loss = prob_loss0 + prob_loss1 + prob_loss2\n","        print(total_giou_loss, total_conf_loss, total_prob_loss)\n","        # total_loss = total_giou_loss + total_conf_loss + total_prob_loss  # total_giou_loss + total_conf_loss + total_prob_loss\n","    gradients1 = tape_giou.gradient(total_giou_loss, model.yolo_model.trainable_variables)\n","    gradients2 = tape_conf.gradient(total_conf_loss, model.yolo_model.trainable_variables)\n","    gradients3 = tape_prob.gradient(total_prob_loss, model.yolo_model.trainable_variables)\n","    opt.apply_gradients(zip(gradients1, model.yolo_model.trainable_variables))\n","    opt2.apply_gradients(zip(gradients2, model.yolo_model.trainable_variables))\n","    opt3.apply_gradients(zip(gradients3, model.yolo_model.trainable_variables))\n","\n","logs = np.array([])\n","for epoch in range(1000):\n","    for x_batch, y_batch_tensor, y_batch_bbox in data_gen:\n","        y_true = [np.zeros(BS), np.zeros(BS), np.zeros(BS)]\n","        # print('train on batch ', y_batch_tensor[0].shape)\n","        losses = model2.train_on_batch([x_batch, *y_batch_tensor, y_batch_bbox], y_true)\n","        loss = losses[0]\n","        print(f'epoch {epoch} losses ' , loss)\n","        if len(logs) > 0 and loss < np.min(logs):\n","            model.yolo_model.save('/content/drive/My Drive/yolov4-giou.h5')\n","        logs = np.append(logs, loss)\n","        # train_step(x_batch, y_batch_tensor, y_batch_bbox)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xqxqbu_XVLa9","colab_type":"code","colab":{}},"source":["# model2.fit(data_gen, \n","#             steps_per_epoch=1,\n","#            epochs=10000\n","#            )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"W6iv_-gHmSte","colab_type":"code","colab":{}},"source":["model.predict('/content/drive/My Drive/yolo-v4-tf.keras/dataset/train_img2/test3.jpg')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-3H2ukQIB4gc","colab_type":"code","colab":{}},"source":["i = np.random.randint(0, 300)\n","path = f'/content/drive/My Drive/yolo-v4-tf.keras/dataset/train_img/BloodImage_00000.jpg'\n","print(path)\n","model.predict(path)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"95zRzZHjW9aS","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]}]}